This chapter explains how the collected data can be processed with the aim of extracting meaningful measures, and how statistical analysis can be used to support significant conclusions. This chapter first introduces common quantitative measures for transparency research, including measures for tracking, privacy, fairness, and similarity. To compute most of these measures, data need to be preprocessed to extract the response variables of interest from the raw collected data, for example using simple transformations or heuristics, machine learning classifiers or natural language processing, or static and dynamic analysis methods for mobile apps. Finally, the chapter explains statistical methods that allow to make meaningful and statistically significant statements about the behavior of the response variables in the experiment.